SpeechEQ搞了个新基准，测AI在对话里能不能听懂语气和情绪，发现模型靠文字猜情绪，安全对齐后还变傻了。想做真情感AI的必看。
现有机器情感智力评估局限于孤立文本或被动声学感知，忽略多轮对话中的跨模态推理。新框架SpeechEQ基于EQ-i 2.0理论构建了2265个对话数据集，覆盖15个EQ子量表，并引入多轮评估协议和SEQ分数。实验发现端到端语音语言模型优于级联系统，但仍存在文本依赖的“模态捷径”、对齐引发的“安全陷阱”和“上下文失忆”三大瓶颈。
SpeechEQ: Benchmarking Emotional Intelligence Quotient in Socially Aware Voice Conversational Models
As multimodal conversational systems increasingly engage in spoken interaction, their ability to navigate paralinguistic social cues has become a critical bottleneck for natural human-AI communication. However, existing evaluations of machine emotional intelligence assess reasoning exclusively through isolated text or passive acoustic perception, overlooking the complex cross-modal reasoning required for active, multi-turn dialogue. We introduce \textsc{SpeechEQ}, a comprehensive framework designed to evaluate the sociolinguistic reasoning of Speech-Language Models (SLMs). The framework includes a validated dataset of 2,265 dialogues across 15 Emotional Quotient (EQ) subscales grounded in EQ-i 2.0 theory, along with a multi-turn evaluation protocol measured by our proposed Spoken EQ (SEQ) score inspired by human EQ assessments. Experiments show limitations in how both existing Speech Emotion Recognition and end-to-end Speech-Language Models understand and apply paralinguistic cues through speech. While end-to-end architectures outperform cascaded systems, \textsc{SpeechEQ} reveals that current multimodal models remain bottlenecked by a text-reliant ``modality shortcut,'' an alignment-induced ``safety trap,'' and ``contextual amnesia,'' highlighting the barriers to truly emotionally aware AI. Our benchmark can be accessed at https://huggingface.co/datasets/SpeechEQ/SpeechEQ and demo page at https://binomial14.github.io/speecheq-demo/